Applications and prospects of machine learning in perioperative transfusion medicine
10.13303/j.cjbt.issn.1004-549x.2025.10.023
- VernacularTitle:机器学习在围手术期输血医学中的应用与展望
- Author:
Rui FAN
1
;
Xiaoying ZHANG
1
;
Weiwei SHANG
1
;
Wenfei TANG
1
;
Haimei MA
1
Author Information
1. Department of Blood Transfusion, Beijing Tsinghua Changgeng Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing 102218, China
- Publication Type:Journal Article
- Keywords:
machine learning;
perioperative period;
blood transfusion prediction;
blood management;
clinical decision support
- From:
Chinese Journal of Blood Transfusion
2025;38(10):1450-1456
- CountryChina
- Language:Chinese
-
Abstract:
This paper systematically reviews the application progress of machine learning in perioperative transfusion medicine, focusing on its significant achievements in identifying transfusion risk factors, accurately predicting transfusion requirements, and enabling dynamic monitoring with real-time feedback. It also examines the methodologies, performance metrics, and clinical significance of constructing machine learning models across various surgical specialties, including orthopaedics, cardiac surgery, trauma, and obstetrics. The review further analyzes major challenges currently facing the field, including data bias, model overfitting and interpretability issues, alongside privacy and ethical concerns. Finally, it outlines future directions, highlighting how multimodal data fusion, deep learning applications, multicentre validation, and interdisciplinary collaboration are poised to significant potential for advancing the clinical translation of intelligent transfusion models, achieve personalized precision transfusion management, and enhance patient safety and therapeutic outcomes.
